1. Field of the invention
The invention relates to image processing and, more particularly, to an apparatus and method for feature-based contrast enhancement.
2. Description of the Related Art
Throughout the specification and claims, the following terms take the meanings explicitly associated herein, unless the context clearly dictates otherwise. The term “luma” refers to the component of an input image data value that is correlated to the perceived intensity of a displayed data value. Wherein, the input image data itself may be analog/digital in nature and finally displayed on an analog/digital display such as a CRT/LCD. The term “chroma” refers to the component of the input image data value that is correlated to the perceived color of the displayed data value. Hue and saturation are two commonly used color perception that together define the chroma data. The term “dynamic contrast enhancement” refers to a dynamic adjustment of image luma contrast according to image luma level distribution (histogram).
In conventional dynamic contrast enhancement apparatus, the following three modes are widely used to perform contrast enhancement. The first is a picture-based mode. Contrast enhancement is performed based on a global histogram associated with a frame or a field. The background noise could be enhanced and the detailed features could be suppressed with respect to the processed images. The second is a region-based mode. A frame or a field is divided into multiple regions, and each region respectively has a region histogram. Contrast enhancement is performed by using one of the following two approaches. (1) Histogram equalization is performed for the corresponding region histogram of each region. The detailed features and the background noise could be enhanced simultaneously with respect to the processed images. (2) A global histogram is generated by disregarding the regions having over-concentrated histograms and using the histograms of the rest regions. Its background noise could be effectively suppressed. The third is a picture-region-based mode, which combines methods of the picture-based mode and the region-based mode. Histogram equalization is performed according to a global histogram and then local enhancement is performed according to the corresponding region histogram of each region. Thus, the detailed features and the background noise could be enhanced simultaneously with respect to the processed images.
Although some contrast enhancement methods related to the region-based mode can avoid enhancing the background noise and further enhancing the detailed image features, high hardware cost and huge volume of computation will be the problems. For example, if each frame or each field is divided into M regions and each region histogram includes N bins, the hardware cost of one contrast enhancement apparatus operating in a region-based mode will be M times higher than that of another contrast enhancement apparatus operating in a picture-based mode.
Therefore, it is desirable to suppress the background noise and enhance the detailed image features without increasing the hardware cost.
On the other hand, contrast is generally defined as the ratio of the brightest pixel value to the darkest pixel value in an image. Many experimental results show that an S-shaped user transfer curve (suppressing dark pixel value and enhancing bright pixel value) can improve the human perceptions of image contrast. According to the above definition, the S-shaped user transfer curve having characteristics of suppressing dark pixel value and enhancing bright pixel value indeed increases the contrast ratio. FIG. 1 shows an example of a dark scene histogram, a dark scene transfer curve, an S-shaped user transfer curve and a final transfer curve. For most of dark scene images, at first, a conventional contrast enhancement apparatus performs proper stretching on the dark scene transfer curve. Accordingly, as shown in FIG. 1, the yout values of the dark scene transfer curve are slightly greater than those of the 45-degree dotted line (having a slope equal to one). After that, the conventional dynamic contrast enhancement apparatus adjusts the dark scene transfer curve according to the S-shaped user transfer curve, or directly combines the dark scene transfer curve with the S-shaped user transfer curve to generate a final transfer curve. As can be observed from
FIG. 1, the S-shaped user transfer curve counteracts the effect of the brightness stretching of the dark scene transfer curve, making the final transfer curve approximate to a 45-degree line. In other words, conventional dynamic contrast enhancement apparatus made vain attempts to perform contrast enhancement for the dark scene images.